Part 1 Fundamentals: basics of neuroscience and artificial neuron models graphs algorithms. Part 2 Feedforward networks: perceptrons and LMS algorithm complexity of learning using feedforward networks adaptive structure networks. Part 3 Recurrent networks: symmetric and asymmetric recurrent network competitive learning and self-organizing networks. Part 4 Applications of neural networks: neural networks approach to solving hard problems.